






import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
							import torch

							from transformers import AutoModelForImageClassification

if is_vision_available():
							from transformers import AutoImageProcessor

@require_torch
@require_vision
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@slow
				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'      )
											_lowercase							:   List[Any] 							=   AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'      )
											model.to(_lowerCAmelCase      )

											from datasets import load_dataset

											_lowercase							:   Union[str, Any] 							=   load_dataset('nielsr/rvlcdip-demo'      )

											_lowercase							:   Any 							=   dataset['train'][0]['image'].convert('RGB'      )

											_lowercase							:   List[str] 							=   image_processor(_lowerCAmelCase ,						return_tensors='pt'      ).to(_lowerCAmelCase      )

											# forward pass
											with torch.no_grad():
																		_lowercase							:   Dict 							=   model(**_lowerCAmelCase      )
																		_lowercase							:   Any 							=   outputs.logits

											_lowercase							:   str 							=   torch.Size((1, 1_6)      )
											self.assertEqual(logits.shape ,						_lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   torch.tensor(
											    [-0.41_58, -0.40_92, -0.43_47] ,						device=_lowerCAmelCase ,						dtype=torch.float ,						)
											self.assertTrue(torch.allclose(logits[0, :3] ,						_lowerCAmelCase ,						atol=1E-4      )      )





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()